1. Field of the Invention
The invention relates to non-invasive blood analyte predication using near IR tissue absorption spectra. More particularly, the invention relates to a method of classifying sample spectra into groups having a high degree of internal consistency to minimized prediction error due to spectral interferents.
2. Description of Related Technology
The goal of noninvasive blood analyte measurement is to determine the concentration of targeted blood analytes without penetrating the skin. Near infrared (NIR) spectroscopy is a promising noninvasive technology that bases measurements on the absorbance of low energy NIR light transmitted into a subject. The light is focused onto a small area of the skin and propagates through subcutaneous tissue. The reflected or transmitted light that escapes and is detected by a spectrometer provides information about the contents of the tissue that the NIR light has penetrated and sampled. The absorption of light at each wavelength is determined by the structural properties and chemical composition of the tissue. Tissue layers, each containing a unique heterogeneous chemistry and particulate distribution, result in light absorption and scattering of the incident radiation. Chemical components such as water, protein, fat and blood analytes absorb light proportionally to their concentration through unique absorption profiles. The sample tissue spectrum contains information about the targeted analyte, as well as a large number of other substances that interfere with the measurement of the analyte. Consequently, analysis of the analyte signal requires the development of a mathematical model for extraction of analyte spectral signal from the heavily overlapped spectral signatures of interfering substances. Defining a model that produces accurate compensation for numerous interferents may require spectral measurements at one hundred or more frequencies for a sizeable number of tissue samples.
In equation 7, T is a matrix representing the concentration or magnitude of interferents in all samples, and P represents the pure spectra of the interfering substances or effects present. Any spectral distortion can be considered an interferent in this formulation. For example, the effects of variable sample scattering and deviations in optical sampling volume must be included as sources of interference in this formulation. The direct calibration for a generalized least squares model on analyte y is
yGLS=(KTxe2x80x94xe2x88x921K)xe2x88x921KTxe2x80x94xe2x88x921(xxe2x88x92k0)xe2x80x83xe2x80x83(8)
where _ is defined as the covariance matrix of the interfering substances or spectral effects, Û is defined as the measurement noise, x is the spectral measurement, and k0 is the instrument baseline component present in the spectral measurement.
Accurate noninvasive estimation of blood analytes is also limited by the dynamic nature of the sample, the skin and living tissue of the patient. Chemical, structural and physiological variations occur produce dramatic changes in the optical properties of the measured tissue sample. See R. Anderson, J. Parrish. The optics of human skin, Journal of Investigative Dermatology, vol. 77(1), pp. 13-19 (1981); and W. Cheong, S. Prahl, A. Welch, A review of the optical properties of biological tissues, IEEE Journal of Quantum Electronics, vol. 26(12), pp. 2166-2185 (December 1990); and D. Benaron, D. Ho, Imaging (NIRI) and quantitation (NIRS) in tissue using time-resolved spectrophotometry: the impact of statically and dynamically variable optical path lengths, SPIE, vol. 1888, pp.10-21 (1993); and J. Conway, K. Norris, C. Bodwell, A new approach for the estimation of body composition: infrared interactance, The American Journal of Clinical Nutrition, vol. 40, pp. 1123-1140 (December 1984); and S. Homma, T. Fukunaga, A. Kagaya, Influence of adipose tissue thickness in near infrared spectroscopic signals in the measurement of human muscle, Journal of Biomedical Optics, vol. 1(4), pp. 418-424 (October 1996); and A. Profio, Light transport in tissue, Applied Optics, vol. 28(12), pp. 2216-2222 (June 1989); and M. Van Gemert, S. Jacques, H. Sterenborg, W. Sta, Skin optics, IEEE Transactions on Biomedical Engineering, vol. 36(12), pp. 1146-1154 (December 1989); and B. Wilson, S. Jacques, Optical reflectance and transmittance of tissues: principles and applications, IEEE Journal of Quantum Electronics, vol. 26(12), pp. 2186-2199.
Overall sources of spectral variations include the following general categories:
1. Co-variation of spectrally interfering species. The near infrared spectral absorption profiles of blood analytes tend to overlap and vary simultaneously over brief time periods. This overlap leads to spectral interference and necessitates the measurement of absorbance at more independently varying wavelengths than the number of interfering species.
2. Sample heterogeneity. The tissue measurement site has multiple layers and compartments of varied composition and scattering. The spectral absorbance versus wavelength measurement is related to a complex combination of the optical properties and composition of these tissue components. Therefore, the spectral response with changing blood analyte concentration is likely to deviate from a simple linear model.
3. State Variations. Variations in the subject""s physiological state effect the optical properties of tissue layers and compartments over a relatively short period of time. Such variations, for example, may be related to hydration levels, changes in the volume fraction of blood in the tissue, hormonal stimulation, skin temperature fluctuations and blood hemoglobin levels. Subtle variations may even be expected in response to contact with an optical probe.
4. Structural Variations. The tissue characteristics of individuals differ as a result of factors that include hereditary, environmental influences, the aging process, sex and body composition. These differences are largely anatomical and can be described as slowly varying structural properties producing diverse tissue geometry. Consequently, the tissue of a given subject may have distinct systematic spectral absorbance features or patterns that can be related directly to specific characteristics such as dermal thickness, protein levels and percent body fat. While the absorbance features may be repeatable within a patient, the structural variations in a population of patients may not be amenable to the use of a single mathematical calibration model. Therefore, differences between patients are a significant obstacle to the noninvasive measurement of blood analytes through NIR spectral absorbance.
In a non-dispersive system, variations similar to (1) above are easily modeled through multivariate techniques such as multiple linear regression and factor-based algorithms. Significant effort has been expended to model the scattering properties of tissue in diffuse reflectance, although the problem outlined in (2) above has been largely unexplored. Variation of the type listed in (3) and (4) above causes significant nonlinear spectral response for which an effective solution has not been reported. For example, several reported methods of noninvasive glucose measurement develop calibration models that are specific to an individual over a short period of time. See K. Hazen, Glucose determination in biological matrices using near-infrared spectroscopy, Doctoral Dissertation, University of Iowa (August 1995); and J. Burmeister, In vitro model for human noninvasive blood glucose measurements, Doctoral Dissertation, University of Iowa (December 1997); and M. Robinson, R. Eaton, D. Haaland, G. Koepp, E. Thomas, B. Stallard and P. Robinson, Noninvasive glucose monitoring in diabetic patients: a preliminary evaluation, Clin. Chem, vol. 38 (9), pp. 1618-1622 (1992). This approach avoids modeling the differences between patients and therefore cannot be generalized to more individuals. However, the calibration models have not been tested over long time periods during which variation of type (4) may require recalibration. Furthermore, the reported methods have not been shown to be effective over a range of type (3) variations.
The invention provides a Multi-Tier method for classifying tissue absorbance spectra that localizes calibration and sample spectra into local groups that are used to reduce variation in sample spectra due to co-variation of spectral interferents, sample heterogeneity, state variation and structural variation. Measurement spectra are associated with localized calibration models that are designed to produce the most accurate estimates for the patient at the time of measurement. Classification occurs through extracted features of the tissue absorbance spectrum related to the current patient state and structure.
The invention also provides a method of developing localized calibration models from tissue absorbance spectra from a representative population of patients or physiological states of individual patients that have been segregated into groups. The groups or classes are defined on the basis of structural and state similarity such that the variation in tissue characteristics within a class is smaller than the variation between classes.